Manipulation and grasp have been active research areas in robotics for several decades. One of the primary goals of the research is the choice of an appropriate grasp, in terms of task requirement and stability properties, given an object associated with a manipulation task to be performed. Such a problem is referred to as the grasp synthesis problem. To solve this problem, different approaches and algorithms have been developed for the robotic hand to execute a stable manipulation task.
One solution to the grasp synthesis problem is grasp planning. Grasp planning uses optimization mathematics to search for the optimal contact placement on an object, which gives rise to difficulty in choosing a quality criterion for the optimization procedure. One widely-used quality criterion is the force closure that measures the capability of a grasp to apply a certain amount of force on an object to resist disturbances in any direction, defined as the radius of the largest six-dimensional wrench space sphere centered at the origin and enclosed with the unit grasp wrench space (GWS).
The above approach is task-independent and, therefore, an evenly distributed disturbance in all directions is assumed. In many manipulation tasks, however, such as drinking, writing, and handling a screwdriver, a task-related grasp criterion must be applied for the choice of appropriate grasp configurations for different task requirements. A manipulation task refers to the process of moving or rearranging objects within an environment. To perform a task, the object to be manipulated would interact with the environment and suffer from outside disturbance. For example, the screwdriver can experience disturbance in the form of resistance forces of a screw that is being driven by the screwdriver. A task-oriented grasp should be able to resist the force disturbance required for a task. That is, the grasp should be maintained (i.e., the object should not be dropped) in spite of the force disturbance.
One typical task-oriented grasp method is to choose a suitable task wrench space (TWS) and then measure how well it can be fitted into a GWS. A challenge with this approach is that it is difficult to model the disturbance associated with the task to obtain the TWS and sensors are required to measure the contact regions and contact normals in human demonstration to obtain the TWS in reality. For this reason, most approaches empirically approximate the TWS rather than actually measure it.
Another difficulty of task-oriented grasp planning is the computational complexity of searching in a high-dimensional hand configuration space. It is, therefore, natural to introduce human experience relative to a task. Data gloves have been used in prior research to map human-hand to robotic-hand workspace and capture the TWS in virtual reality. A database of candidate grasps was considered, and grasps were evaluated by a task-related quality measure. However, the correspondence problem has been a crucial issue to map between two different configuration spaces of the human hand and the robotic hand. Other research has involved searching for candidate grasps using a shape-matching algorithm and evaluating the grasps using a task-oriented criterion. However, the TWS is also modeled by experience rather than actually measuring it from the demonstrated task.
From the above discussion, it can be appreciated that it would be desirable to have an alternative approach for planning a task-oriented grasp that can withstand disturbance from the environment associated with performing the task.